Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package which includes all methods discussed in this paper.

翻译：可视化是传达任何旨在估计因果效应的研究结果的关键环节。在时间事件结局的研究中，最流行的可视化方法是绘制按目标变量分层的生存曲线。但当目标变量为连续变量时，此方法不再适用。简单的变通方案，如将连续协变量分类并绘制每类别的生存曲线，可能导致对主效应的错误描述。为此，我们提出一种新型图形——生存区域图，可同时展示生存概率随时间的变化及其作为连续协变量的函数关系。该图形基于合适的时间事件模型利用g-计算方法获取相关估计值。通过g-计算，这些估计值可在无需额外努力的情况下调整混杂因素，从而在标准因果可识别性假设下实现因果解释。若这些假设不满足，所提出的图形仍可用于描述非因果关联。我们利用德国一项大型观察性研究数据（该研究探索踝臂指数对生存的影响）对所提出图形进行了说明，并将其与简单替代方案进行比较。为促进这些图形的使用，我们还开发了包含本文所有方法的contsurvplot R软件包。